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Nowadays, the need for cloud computing has increased due to the exponential growth in information transmission. Cybercriminals are persistent in their efforts to breach cloud environments, even with security measures in place to protect data stored in the cloud. To address this challenge, an enhanced authentication approach is needed for enhanced security. In order to protect user privacy and anonymity in cloud environments, the study presents a novel technique called Hyperelliptic Curve-based Anonymous Ring Signature (HCARS). Moreover, Blockchain technology is utilized to securely record timestamps and cryptographic keys. The hashing functions in the Blockchain system employ SHA 256 and SHA 512 algorithms. Furthermore, utilizing Ring Learning with Error (RLWE) problems, an Nth degree Truncated Polynomial Ring Units (NTRU)-Based Fully Homomorphic Encryption (NTRU-FHE) Scheme encrypts sensitive data and ensures its integrity. A comparative study between the proposed method and current approaches is done through experimental verification utilizing Java. The results demonstrate that the proposed approach outperforms existing techniques, achieving an encryption time of 6.75 s for an input size of 75 and a decryption time of 5.128 s for the same input size. Similarly, the signature generation time is 125 ms for 100 received messages, block generation time of 10.8 s for 450 blocks, throughput of 98 MB/sec for a record size of 16,384, and total computational time of 403 ms for 20 messages. The results demonstrate the superior performance of the HCARS approach, with significantly reduced encryption, decryption, and signature generation times, as well as improved throughput and computational efficiency. Securing the security and privacy of cloud-based systems in the face of changing cyber threats has been made much easier with the help of the HCARS approach. [ABSTRACT FROM AUTHOR]

Summary: Cloud Computing (CC) and big data are growing technology in the business. Big data is demonstrated in terms of volume, variety, and velocity. CC is employed for storing, processing, and accessing data. Many cryptographic techniques have been developed to enhance big data security in cloud computing. However, security and privacy are the primary concerns in protecting data, as it is highly sensitive. Yet, it faces the major problems of inefficient performance, increased time consumption, and lack of data confidentiality and integrity. To address this issue, proposed Extreme Learning with Projection Relational Algebraic Secured Data Transmission (ELPRA‐SDT) is introduced to secure data transactions from cloud users to cloud servers with enhanced data confidentiality and reduced time consumption for big cloud data. The proposed ELPRA‐SDT consists of two major processes namely registration and key generation. At first, the user's IP address is registered employing a transitive advanced set relation theory graph model in a cloud server (CS) for retrieving the numerous services. The CS generates private and public keys for each registered user's IP address using the Transitive Operational and Time Synchronized Random Winternitz Key generation model. After, the user sends a request to the CS for acquiring data. The CS validates the requested user based on security policy attributes. Second, the Projection Relational Algebraic Signcryption and Unsigncryption algorithm performs signature verification to ensure secure data access for protecting the data. Results of experiments carried out by using Coburg Intrusion Detection Data Sets‐001 dataset in Java. ELPRA‐SDT method is more efficient and more suitable for providing security and privacy to network traces in the Cloud. The result shows maximum performance with data confidentiality by 10% and data integrity by 13%. In addition, delay is reduced by 32%, and data delivery time and communication complexity is decreased by 28% and 24% to other existing methods. [ABSTRACT FROM AUTHOR]

The main aim of this research work is to make it easier for the same group to share and store anonymous data on the cloud securely and effectively. This research work presents verifiable privacy-aware enhanced homomorphic (VPEH) for multiple participants; moreover, the enhanced homomorphic encryption mechanism provides end-to-end encryption and allows the secure computation of data without revealing any data in the cloud. The proposed algorithm uses homomorphic multiplication to compute the hashes product of challenges blocks that make it more efficient, furthermore an additional security model is incorporated to verify the shared data integrity. The VPEH mechanism is evaluated considering parameters such as tag generation, proof generation, and verification; model efficiency is proved by observing the marginal improvisation over the other existing models by varying the number of blocks and number of challenge blocks. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cloud Computing is a new paradigm that provides software and hardware resources based on customers’ needs. However, data security remains a significant concern, acting as a major barrier to its widespread adoption. The objective of this work is to propose a process for assessing data risks in Cloud Computing. The process is based on fuzzy cognitive maps (FCMs) for qualitative reasoning. Risk assessment is a crucial aspect of Cloud Computing. Our proposed system involves three main steps based on expert knowledge. In the first step, a global FCM was built and validated to identify threats at the Cloud Computing level. Then the second step was devoted to calculate the consequences by inference using FCM Expert. The obtained results show that the FCM has a remarkable capacity to deal with the uncertainty of the data; by doing so, it becomes possible to model the real system more accurately. The last step is to simulate, analyze the results of the second step in Matlab Simulink and also develop recommendations to reduce the risks in order to improve and increase the security of Cloud data. [ABSTRACT FROM AUTHOR]

This research introduces a novel security framework specifically tailored to enhance data protection during cloud transmission and migration. Our study addresses critical gaps in existing security models by proposing a multi-dimensional system that incorporates advanced encryption techniques, dynamic access control, and continuous security auditing. Notably, this framework excels in ensuring cloud data integrity, confidentiality, and availability--core aspects often compromised under conventional methods. Comparative analysis with existing models in simulated cloud environments reveals that our framework significantly enhances threat detection accuracy, response speed, and resource management efficiency. The findings highlight the system's capability to reduce security vulnerabilities while optimizing operational overhead, presenting a substantial improvement over traditional security solutions. This innovative approach, marked by improved scalability and flexibility, is poised to revolutionize cloud data security practices across various industries, prompting further research into robust cloud computing security methodologies. [ABSTRACT FROM AUTHOR]

The rise of cloud computing has transformed the way data is stored and managed, yet it has also brought about major security issues, especially concerning the secure transfer of data within cloud systems. In response to these challenges, this research develops a comprehensive cyber-security trust model that provides multi-risk protection for secure data transmission in cloud computing, ensuring the highest level of privacy and data security. This innovative approach aims to ensure the secure transmission of data in cloud computing while harnessing the combined strengths of Quantum Key Distribution (QKD) and Advanced Encryption Algorithm. As cloud environments become integral to modern business operations, safeguarding data against a multitude of security risks, including traditional and emerging threats, is paramount. The Cyber-Security Trust Model leverages blockchain to establish a transparent and tamper-resistant ledger of all data transactions within the cloud. This blockchain layer enhances data integrity, auditability, and traceability while also providing a decentralized and trust-based framework for authentication and authorization. The Multi-Risk Protection Model incorporates both Quantum Key Distribution (QKD) and a Modified Advanced Encryption Standard (MAES) to offer multi-layered defence mechanisms. Through rigorous testing and analysis, this study demonstrates the feasibility and effectiveness of the proposed Cyber-Security Trust Model with a Merkle tree-based solution for data integrity verification. It makes a significant impact on the field of secure data transmission in cloud computing by providing strong protection against a constantly changing set of security threats. MATLAB is employed to conduct rigorous experiments, analyse results, and validate the model's performance in various cloud computing scenarios. The findings of the proposed study show the proposed method, combining Quantum Key Distribution (QKD) and Modified AES (MAES), stands out with exceptional performance, featuring encryption and decryption times of 2.25ms and 1.071ms, respectively. The proposed system outperforms all others, boasting an impressive accuracy rate of 99.84%. This research signifies a ground-breaking advancement in cloud computing security, addressing a spectrum of traditional and emerging threats through a multi-risk protection model incorporating Quantum Key Distribution (QKD) and MAES while demonstrating exceptional performance in rigorous experiments. [ABSTRACT FROM AUTHOR]

Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply‐chain supervision and collaboration, Internet of Vehicles, real‐time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token‐based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man‐in‐the‐middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. [ABSTRACT FROM AUTHOR]

The widespread adoption of cloud computing has dramatically altered how data is stored, processed, and accessed in an era. The rapid development of digital technologies characterizes all this. The widespread adoption of cloud services has introduced new obstacles to guaranteeing secure and expeditious access to sensitive data. Organizations of all types find user-friendly and cost-effective solutions crucial, which is why they consider cloud services essential. The availability of the cloud hampers access control security in systems that are constantly and remotely changing. Conventional methods of access control are efficient, but the advanced world of technology exposes them to more threats. Applying blockchain technology to cloud access control systems, which are decentralized, transparent, and tamper-proof, has overcome these challenges. This paper aims to discuss the potential of blockchain in enhancing access management, security and trust in cloud computing. Besides, this scholarly article reviews the evolving area of blockchain-based access control systems and synthesizes the findings of 118 selected papers from various academic repositories. Based on this systematic review of the studies, twelve different types of blockchain-based access control paradigms can be identified. This work provides a critical analysis of the research on blockchain technology in access control systems, with a focus on scalability, compatibility, and security challenges. It also highlights areas that require further research and proposes directions for future research to advance this rapidly growing area of scholarship. [ABSTRACT FROM AUTHOR]

The expansion of the cloud computing market has provided a breakthrough in efficiently storing and managing data for individuals and companies. As personal and corporate data move to the cloud, diverse attacks targeting the cloud have also increased for heist beneficial information. Therefore, cloud service providers offer protective environments through diverse security solutions. However, security solutions are limited in preventing advanced attacks because it is challenging to reflect the environment of each user. This paper proposes a Cloud Intrusion Detection System (C-IDS) that adapts to each user's cloud environment and performs real-time attack detection using Natural Language Processing (NLP). Notably, the C-IDS learns the deployed client environment logs and detects anomalies using the Seq2Seq model with BI-LSTM and Bahdanau attention. We used multiple domain datasets, Linux, Windows, Hadoop, OpenStack, Apache, OpenSSH, and CICIDS2018 to verify the performance of the C-IDS. C-IDS consists of a 'recognition' that identifies logs in the deployed environment and a 'detection' that discovers anomalies. The recognition results showed an average accuracy of 98.2% for multiple domain datasets. Moreover, the detection results based on the trained model exhibited an average accuracy of 94.2% for the Hadoop, OpenStack, Apache, and CICIDS2018 datasets. [ABSTRACT FROM AUTHOR]

Modern technologies of computing cloud are showing great promise, but at the same time create new security challenges that hinder full acceptance. Given that most of these services often use cloud networks as channels for communication, securing data transmission is crucial. This paper introduce a new hybrid encryption algorithm, the proposed two‐layered PRC6 cipher, tailored address security concerns in cloud computing environments with minimal resource constraints. The PRC6 cipher incorporates enhancements from Cha‐cha into an extension of the RC6 cipher. PRC6 implements double encryption. At the first level, the plain text is divided into four equal parts, each encrypted by processes derived from RC6, which include shifting, summation, modulo arithmetic, and XOR with a generated key. The second level incorporates a Quarter round function, among others, to further obscure the encoded message. PRC6 is implemented in a parallel computing model to significantly reduce overall computation time, especially important for lightweight applications. Experimental results show that the algorithm can achieve a high level of security for cloud workloads. It activates parallel mode in just seven encryption rounds, cutting calculation time to 50% in a matter of seconds. Performance evaluations against popular encryption standards also indicate that PRC6 offers promising security benefits when computational resources are limited. This hybrid approach presents a viable solution for strengthening data protection in modern cloud systems and it stand against the most popular attacks like brute force. [ABSTRACT FROM AUTHOR]

The growing industry and its complex and large information sets require Big Data (BD) technology and its open-source frameworks (Apache Hadoop) to (1) collect, (2) analyze, and (3) process the information. This information usually ranges in size from gigabytes to petabytes of data. However, processing this data involves web consoles and communication channels which are prone to intrusion from hackers. To resolve this issue, a novel machine learning (ML)-based security-centric approach has been proposed to evade cyber-attacks on the Hadoop ecosystem while considering the complexity of Big Data in Cloud (BDC). An Apache Hadoop-based management interface "Ambari" was implemented to address the variation and distinguish between attacks and activities. The analyzed experimental results show that the proposed scheme effectively (1) blocked the interface communication and retrieved the performance measured data from (2) the Ambari-based virtual machine (VM) and (3) BDC hypervisor. Moreover, the proposed architecture was able to provide a reduction in false alarms as well as cyber-attack detection. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cloud computing has gained immense popularity for delivering computing resources over the internet, enabling organizations to efficiently and cost-effectively store, process, and access data from any location worldwide. However, the security of data stored and processed outside of the organization's premises has raised concerns. In order to address the changing threat landscape and guarantee the integrity and confidentiality of data in the cloud environment, we address the latest developments and best practices in cloud security in this article, which delves into the complex world of security threats and related countermeasures for cloud computing in the contemporary era of digital transformation. Offered a thorough examination of cloud computing security risks and solutions in the age of digital transformation. The paper defines cloud computing, explores its advantages and disadvantages, identifies major security threats in cloud computing, including data breaches, insider threats, and denial-of-service attacks, and discusses the countermeasures that can be implemented to mitigate these threats. The most popular cryptographic algorithms in cloud computing environments as part of their defense are presented. Special emphasis is given on the mode of operation of these algorithms is presented so that we can choose the appropriate one in each situation. Finally, access control and monitoring are presented in order to have a thorough understanding of protection methods in cloud computing environments. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth discussion on the comprehensive requirements analysis, design implementation, algorithm optimization, and experimental evaluation of an electric power marketing information system, aiming to build a modern information system that is efficient, secure, and user-friendly. In the requirements analysis phase, the importance of business process optimization, data management analysis, security compliance, system integration and scalability is emphasized, while the diversified needs of end customers are considered. For the design and implementation part, the system architecture is based on microservices and cloud-native technologies to ensure high performance and security; and modularized development is achieved through Spring Boot, Vue.js and other technology stacks. For algorithm optimization, LSTM is used for power demand prediction and anomaly detection by combining integrated learning and self-encoder, which improves the prediction accuracy and anomaly identification capability. Experimental evaluation shows that the system demonstrates good performance, security and scalability in cloud computing environment, and the cost-effectiveness is significantly better than traditional deployment. [ABSTRACT FROM AUTHOR]

Users can purchase virtualized computer resources using the cloud computing concept, which is a novel and innovative way of computing. It offers numerous advantages for IT and healthcare industries over traditional methods. However, a lack of trust between CSUs and CSPs is hindering the widespread adoption of cloud computing across industries. Since cloud computing offers a wide range of trust models and strategies, it is essential to analyze the service using a detailed methodology in order to choose the appropriate cloud service for various user types. Finding a wide variety of comprehensive elements that are both required and sufficient for evaluating any cloud service is vital in order to achieve that. As a result, this study suggests an accurate, fuzzy logic-based trust evaluation model for evaluating the trustworthiness of a cloud service provider. Here, we examine how fuzzy logic raises the efficiency of trust evaluation. Trust is assessed using Quality of Service (QoS) characteristics like security, privacy, dynamicity, data integrity, and performance. The outcomes of a MATLAB simulation demonstrate the viability of the suggested strategy in a cloud setting. [ABSTRACT FROM AUTHOR]

The growing complexity of security threats and the pervasive prevalence of cyberattacks have become more apparent in the present era, and the advent of big data, characterized by its distinctive features, has introduced layers of complexity to security tasks. Intrusion Detection Systems (IDSs) constitute a crucial line of defense, but their adaptation to the realm of big data is imperative. While traditional Machine Learning (ML)-based IDSs have been pivotal in detecting malicious patterns, they are often incapable to keep pace with the demands of expansive big data networks. This paper proposes a novel decentralized Multi-Agent Reinforcement Learning (MARL)-based IDS designed to address the specific challenges posed by big data. Our solution employs decentralized cooperative MARL, securing communicative channels throughout the detection process and concurrent data preprocessing which significantly reduces the overall processing time. Furthermore, the integration of Cloud computing and Big Data streaming techniques further facilitates real-time intrusion detection as cloud's resources allow rapid pre-process and analyse of massive data streams using powerful clusters. Likewise, Big Data streaming techniques ensure that potential intrusions are identified and addressed as they occur. Experimental results, conducted on the widely recognized NSLKDD benchmark dataset, demonstrate the superiority of our solution over other state-of-the-art approaches for big data networks, achieving an accuracy rate of 97.44%. [ABSTRACT FROM AUTHOR]

The study proposes an improved data deduplication technique in order to address storage issues in cloud computing. As a developing technology, cloud computing provides a range of services, such as databases, servers, storage, and analytics. Cloud service providers must store data from multiple customers on the same server in order to maximize resource utilization. Storage, though, continues to be a major issue for both providers and clients. Within this framework, data deduplication becomes an essential method for removing duplicate data from datasets. The Two Threshold Two Divisor (TTTD) algorithm has been used for deduplication in earlier techniques. This study presents a novel approach based on five divisors: minimum, maximum, main, paragraph, and dot divisors, with the goal of increasing efficiency. The purpose of these divisors is to find redundant sentences and paragraphs in order to reduce chunk size and running time. Experimental results provide a thorough evaluation of the proposed approach, showing its superior performance over the state-of-the-art deduplication techniques. This work offers a viable approach to efficient data management and adds to the continuing efforts to maximize storage utilization in cloud environments. [ABSTRACT FROM AUTHOR]

Cloud computing has transformed the way businesses and individuals manage, store, and process data, offering scalable, on-demand resources that drive innovation and efficiency. However, the adoption of cloud services also introduces significant cybersecurity challenges, particularly in the areas of data protection and privacy. This paper provides a comprehensive analysis of the vulnerabilities inherent in cloud environments and explores various advanced techniques for safeguarding data and ensuring privacy. Key areas of focus include encryption (for data at rest, in transit, and in use), access control and identity management, data masking, anonymization, secure multi-party computation, homomorphic encryption, and zero-knowledge proofs. The paper further examines secure cloud architecture frameworks, emphasizing the role of security-by-design principles, segmentation, isolation, and the use of security zones to enhance overall system security. A comparative analysis evaluates the effectiveness of different security measures based on criteria such as performance, scalability, and ease of implementation. The discussion also highlights emerging trends and technologies, including AI-driven security solutions and quantum-resistant encryption, as well as the evolving regulatory landscape and its impact on cloud security strategies. The findings underscore the critical importance of a multi-layered security approach in mitigating risks and protecting sensitive data in cloud environments. The paper concludes with recommendations for future research and development to address ongoing and emerging challenges in cloud cybersecurity, emphasizing the need for continuous innovation to keep pace with the evolving threat landscape. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing has attracted great interest in various scientific and technical fields recently as one of the widely adopted networking technologies. Despite their many benefits and applications, it still faces many security and trust challenges, including managing and controlling services, privacy, data integrity in distributed databases, data backup, and synchronization. Moreover, due to its centralized architecture, and lack of transparency and traceability, the results of the trust assessment cannot be fully recognized by all users. However, creating a trust‐based transaction environment has become its key factor. Blockchain, with its nature of decentralization and security, can be leveraged to address these challenges and build a distributed and decentralized trust architecture, due to the underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation. This article makes a comprehensive study of how blockchain is applied to deliver security services in the cloud computing model, focusing on up‐to‐date approaches, opportunities, and future directions. This survey also discusses the benefits of the technical fusion of blockchain and cloud. It provides a classification of proposed systems based on privacy, data sharing, authentication, and access control, as well as auditing and data integrity. Finally, the main conclusions of this study will be the challenges and future directions to stimulate further research in this promising field. [ABSTRACT FROM AUTHOR]

The study delves into the security architecture of OpenStack, an open-source cloud platform that is increasingly prevalent in modern computing environments. Its primary goal is to rigorously assess and confirm hypotheses about OpenStack's security infrastructure while identifying vulnerabilities and potential threats using a comprehensive security evaluation framework. The study utilizes a multifaceted security assessment methodology to analyze both private and public cloud deployments of OpenStack. This methodology involves various techniques, including vulnerability scanning, penetration testing, and analysis of security policies and configurations. Benchmarking against industry standards and previous studies further strengthens the analytical framework, ensuring a thorough exploration of various dimensions of OpenStack security. The assessment revealed that OpenStack has a robust security posture, with vulnerabilities detected in only 2% of cases across both private and public cloud deployments. The study also found a resilience rate of 95% against common security challenges. The comprehensive analysis covered various dimensions of OpenStack security, providing valuable insights into the platform's security resilience and vulnerabilities, thereby significantly contributing to the body of knowledge in cloud security research. The research underscores the importance of implementing robust security protocols in OpenStack environments to ensure the reliability of cloud infrastructure. Regular security updates and adherence to best practices can strengthen the security posture of OpenStack deployments. The insights from this study can inform the development of guidelines and policies aimed at enhancing security practices in cloud computing environments. Overall, the study evaluates the security framework of OpenStack and emphasizes the significance of implementing robust security measures to ensure the dependability of cloud infrastructure, guiding the creation of recommendations and superior practices for strengthening security in cloud computing environments. [ABSTRACT FROM AUTHOR]

Users of computer networks may benefit from cloud computing, which is a fairly new abstraction that offers features like processing as well as the sharing and storing of data. As a result of the services it provides, cloud computing is drawing significant investments from across the world. Despite this, Cloud Computing Security continues to be one of the most important issues for businesses and consumers that use cloud computing systems. A few of the security flaws that are associated with cloud computing were passed down from earlier computer systems. In contrast, the other flaws were brought about by the distinctive qualities and design of cloud computing. The newly developed platform has measures that restrict data access to just those users who are authorized to do so. Using the user's identification and authentication/authorization information, a third-party service is responsible for managing access to the data. This service checks on all requests. Sensitive information and facts pertaining to users are encrypted both while in transit and while being stored. The platform was put into operation, analysed, and compared to other cloud platforms that were already in existence in terms of how effective it was in comparison to other platforms. When compared to the other security platforms, the findings demonstrated that this platform performed as anticipated in a relatively short amount of time and offered robust protection against the acts of an intruder. [ABSTRACT FROM AUTHOR]

Zero-trust security is a security model that assumes no entity is implicitly trusted, regardless of its origin or scope of access. This approach requires continuous verification of all users, devices, and applications before granting access to resources. Cloud computing is a model for delivering IT resources and applications as a service over the Internet. Cloud computing offers many benefits, including scalability, agility, and cost savings. However, cloud computing also introduces new security challenges. This paper proposes a survey-based research methodology to evaluate the usage of clouds in zero-trust security strategies. The paper identifies different zero-trust security solutions, their key features, and the benefits and challenges of implementing these solutions in the cloud. The paper will also discuss the costs and benefits of different zero-trust security solutions. The findings of this research will be valuable for organizations that are considering implementing a zero-trust security strategy in the cloud. The paper will provide guidance on how to choose the best zero-trust security solution for organizational needs and how to implement it effectively. [ABSTRACT FROM AUTHOR]

Cloud Computing services are widely expanded all over the world because of it comfort and reliable services offered to the user on demand. The majority of the users who are using different pervasive devices are directly or indirectly dependent on the cloud services. The consumer data stored at vendor end has lot of security concerns. However, cloud vendors offer many cloud-computing mechanisms explore the data leakages are challenging issues to be addressed in various levels. This paper presents the systematic investigation of data leakages, which occur in cloud services. In our study we present systematic analysis of (i) data leakages in cloud environment, (ii) security issues related to cloud environment, (iii) cloud security mechanisms and finally we present the challenges in cloud security that need proper solutions [ABSTRACT FROM AUTHOR]

To meet growing business needs and exponentially increasing development and maintenance costs, the concept of cloud computing has been proposed and developed rapidly. Cloud computing is a brand-new computing mode that can meet the needs of on-demand distribution and the rapid deployment of computing resources. It can provide strong scalability and applicability through virtualisation technology and elastic technology, and it can adapt to the needs of users in different environments and resources. Through the use of hardware such as cloud sensors, the data collected by various types of sensors can be directly uploaded to the cloud for processing and analysis, so that applications such as management, medical treatment and human–machine cooperation can be provided. However, applications in the cloud have upended traditional security boundaries and will face some unique security challenges. Due to the advantages of generating real data, generative adversarial networks (GANs) have attracted extensive attention in the field of cloud computing, such as data augmentation and encryption. Therefore, this paper reviews GAN-based security and applications in cloud computing. We compare the role of GANs in security and applications in the cloud from multiple dimensions. In addition, we analyse the research trends and future work prospects from the perspective of the algorithm itself, algorithm performance evaluation and cloud computing hardware. [ABSTRACT FROM AUTHOR]

The conventional advanced encryption standard (AES) method requires improvement to accommodate modern security hazards in cloud computing. Outsourcing private and secret information to distant information networks is difficult due to new problems connected with the confidentiality and safety of information. The structure presented by this study has essential components involving improved security and owner-data privacy. The dual-cycle key attribute alters the 128-bit AES method, accelerating encryption at 1000 bits per second. However, a singular circular bar with 800 blocks per second has been used historically. The suggested technique uses less energy and improves the distribution of load, trust, and control of resources on the entire network. The proposed framework calls for the use of AES with 128, 64, 32, and 16 bytes of simple text. Visualizing simulation outcomes shows the technique's capability for obtaining specific quality features. According to the assessments, the proposed framework reduces energy consumption by 14.43 %, connection utilization by 11.53 %, and delay by 15.67 %. As a result, the suggested framework improves protection, decreases resource usage, and shortens latency when delivering computing services over the cloud. The experiment was done with Anaconda Python and Eclipse. [ABSTRACT FROM AUTHOR]

The potential breach of access to confidential content hosted in a university's Private Academic Cloud (PAC) underscores the need for developing new protection methods. This paper introduces a Threat Analyzer Software (TAS) and a predictive algorithm rooted in both an operational model and discrete threat recognition procedures (DTRPs). These tools aid in identifying the functional layers that attackers could exploit to embed malware in guest operating systems (OS) and the PAC hypervisor. The solutions proposed herein play a crucial role in ensuring countermeasures against malware introduction into the PAC. Various hypervisor components are viewed as potential threat sources to the PAC's information security (IS). Such threats may manifest through the distribution of malware or the initiation of processes that compromise the PAC's security. The demonstrated counter-threat method, which is founded on the operational model and discrete threat recognition procedures, facilitates the use of mechanisms within the HIPV to quickly identify cyber attacks on the PAC, especially those employing "rootkit" technologies. This prompt identification empowers defenders to take swift and appropriate actions to safeguard the PAC. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive review and comparative analysis of existing Information Flow Tracking (IFT) tools which underscores the imperative for mitigating data leakage in complex cloud systems. Traditional methods impose significant overhead on Cloud Service Providers (CSPs) and management activities, prompting the exploration of alternatives such as IFT. By augmenting consumer data subsets with security tags and deploying a network of monitors, IFT facilitates the detection and prevention of data leaks among cloud tenants. The research here has focused on preventing misuse, such as the exfiltration and/or extrusion of sensitive data in the cloud as well as the role of anonymization. The CloudMonitor framework was envisioned and developed to study and design mechanisms for transparent and efficient IFT (eIFT). The framework enables the experimentation, analysis, and validation of innovative methods for providing greater control to cloud service consumers (CSCs) over their data. Moreover, eIFT enables enhanced visibility to assess data conveyances by third-party services toward avoiding security risks (e.g., data exfiltration). Our implementation and validation of the framework uses both a centralized and dynamic IFT approach to achieve these goals. We measured the balance between dynamism and granularity of the data being tracked versus efficiency. To establish a security and performance baseline for better defense in depth, this work focuses primarily on unique Dynamic IFT tracking capabilities using e.g., Infrastructure as a Service (IaaS). Consumers and service providers can negotiate specific security enforcement standards using our framework. Thus, this study orchestrates and assesses, using a series of real-world experiments, how distinct monitoring capabilities combine to provide a comparatively higher level of security. Input/output performance was evaluated for execution time and resource utilization using several experiments. The results show that the performance is unaffected by the magnitude of the input/output data that is tracked. In other words, as the volume of data increases, we notice that the execution time grows linearly. However, this increase occurs at a rate that is notably slower than what would be anticipated in a strictly proportional relationship. The system achieves an average CPU and memory consumption overhead profile of 8% and 37% while completing less than one second for all of the validation test runs. The results establish a performance efficiency baseline for a better measure and understanding of the cost of preserving confidentiality, integrity, and availability (CIA) for cloud Consumers and Providers (C&P). Consumers can scrutinize the benefits (i.e., security) and tradeoffs (memory usage, bandwidth, CPU usage, and throughput) and the cost of ensuring CIA can be established, monitored, and controlled. This work provides the primary use-cases, formula for enforcing the rules of data isolation, data tracking policy framework, and the basis for managing confidential data flow and data leak prevention using the CloudMonitor framework. [ABSTRACT FROM AUTHOR]

In light of growing cloud computing usage, this study is designed and implemented on an efficient security routing protocol for cloud-based networks using Cisco Packet Tracer. Cloud computing's shared resources and dynamic scalability make cloud-based networks vulnerable to unwanted access, data breaches, and insider assaults, prompting the research. The research objectives are to identify and categorize security threats, evaluate existing security solutions, propose an enhanced security measures, and validate these solutions through simulations in Cisco Packet Tracer. A mixed-methods approach was adopted, integrating quantitative and qualitative research designs. Primary data were collected through surveys using Google form and network analysis tools within Cisco Packet Tracer, while secondary data is derived from a comprehensive literature review. The study employed a random sampling technique to select participants with relevant expertise in cloud security. Data analysis involved thematic analysis to identify patterns in the literature and content analysis to extract insights from survey responses. Statistical tests were used to analyze quantitative data, and network analysis was conducted on data obtained from Cisco Packet Tracer simulations. Key findings revealed that data breaches, unauthorized access, insider threats, malware, ransomware attacks, and Denial of Service (DoS) attacks were significant security concerns. The survey results indicated a consensus on the importance of specific features in efficient security routing protocols but also highlighted skepticism regarding the effectiveness of existing protocols. The proposed security measures, including the Three-Level Enabled Secret protocol, Encryption protocol, Secure Shell protocol (SSH), and various routing protocols such as EIGRP, RIP, BGP, and OSPF, Trunk protocol, switch-port security protocol were validated through simulations and showed effectiveness in mitigating security threats. The study has both theoretical and practical implications, contributing to the body of knowledge in cloud computing security and providing practical recommendations for organisations to strengthen their cloud security posture. Limitations include the simulation-based approach and the focus on specific security protocols, suggesting areas for further research in real-world implementation and integration with emerging technologies. [ABSTRACT FROM AUTHOR]

Low-rate distributed denial of service attacks, as known as LDDoS attacks, pose the notorious security risks in cloud computing network. They overload the cloud servers and degrade network service quality with the stealthy strategy. Furthermore, this kind of small ratio and pulse-like abnormal traffic leads to a serious data scale problem. As a result, the existing models for detecting minority and adversary LDDoS attacks are insufficient in both detection accuracy and time consumption. This paper proposes a novel multi-scale Convolutional Neural Networks (CNN) and bidirectional Long-short Term Memory (bi-LSTM) arbitration dense network model (called MSCBL-ADN) for learning and detecting LDDoS attack behaviors under the condition of limited dataset and time consumption. The MSCBL-ADN incorporates CNN for preliminary spatial feature extraction and embedding-based bi-LSTM for time relationship extraction. And then, it employs arbitration network to re-weigh feature importance for higher accuracy. At last, it uses 2-block dense connection network to perform final classification. The experimental results conducted on popular ISCX-2016-SlowDos dataset have demonstrated that the proposed MSCBL-ADN model has a significant improvement with high detection accuracy and superior time performance over the state-of-the-art models. [ABSTRACT FROM AUTHOR]

Background: Digital acceleration coupled with unprecedented work disruption (e.g., a Pandemic) have amplified the need for mature IT governance practices to generate planned value from organizations' digital investments. Although the pairwise relationship between mature governance practices, value derived from IT investments, and organizational performance have been examined previously, all three have rarely been simultaneously investigated. Therefore, this paper examines the role of value in the relationship between IT governance mechanisms and organizational performance. Method: A research model that comprehensively conceptualizes the governance mechanism construct is developed and validated. The model is examined using data collected from 250 United States organizations that have invested in cloud computing for over a year. Results: The results reveal that the value generated from an IT investment is germane to understanding the relationship between governance mechanisms and organizational performance. Specifically, the result explains that governance mechanisms help improve organizational performance through cost reduction in IT services, create agility through flexibility in technology service, strengthen IT security and privacy, and effectively redirect IT resources. The results show the more critical role of the relational mechanism and practices related to IT security and privacy in the cloud computing context. Conclusion: The study contributes to IS literature by providing a more unified conceptualization of governance mechanisms and theoretically establishing the importance of governance in effectively governing cloud computing. By providing a guideline to help organizations achieve more value from cloud computing, the study provides implications for practice. The findings empirically show the relational mechanism has the most critical role in creating value from cloud computing. The governance practices help bridge the gap between business and IT, gradual transformative change in the roles and responsibilities, control cloud expenses, security and privacy risks. The findings show that competency is more likely to be achieved from cloud computing investment. [ABSTRACT FROM AUTHOR]

To overcome the shortcomings of traditional security measures in IoT fog computing, The Multiclass Intrusion Detection (MCID) model is put forward. The model's goal is to improve intrusion detection by identifying and classifying different attack types. The behavioral, temporal and anomaly features are fused through SVM-BFE for obtaining the best possible selection of high worth features. Finally we use a Random Forest algorithm to robustly classify them. There is also its adaptability to the ever-changing security demands of fog computing. MCID's ability to improve the cloud security of fog computing is shown by a 4-fold cross validation, which returns performances including precision rates up to 99.43 %, recall about 95 % and F-measures at as much as 97.17 %. Moreover there are specificity rate totals coming in over this whole range that hit close or right [ABSTRACT FROM AUTHOR]

Due to the exponential growth of cloud data and network services, computational resources and cloud data security have one of the most attractive research issues in the real-time cloud environment. Numerous types of cloud services are combined into various domain applications, including, e-health, defence, clinical databases, and so on, for data storage and resource computing. However, a traditional challenge to consider is that the Data Owner (DO) no longer has the ability to modify the access control policy or key policy, resulting in data sharing inflexibility, inefficiency, and key abusers. To address these concerns, we propose an enhanced d level cut-off point-Quantum Secret Sharing (EdLCp-QSS) scheme based on an efficient Monitoring Key Ciphertext-ABE (MKCABE) –access control with Blockchain and Key Controller (EQMBK) mechanism for security. The proposed EQMBK mechanism provides security for monitoring the data from getting accessed by un-authorized users as well as offers updating security when a new user joins the cloud environment. The proposed EQMBK system ensures that data is not accessible by unauthorised users and that security is updated when a new user joins the cloud environment. Finally, experimental simulation reveals that the suggested technique is very efficient in preserving the privacy of the user's data against unauthorised parties in terms of computation time encryption time, system initialization time, key generation time, and decryption time. Furthermore, when compared to state-of-the-art techniques, the proposed methods are more resilient and provide greater performance. [ABSTRACT FROM AUTHOR]

The detection of different types of concept drift has wide applications in the fields of cloud computing and security information detection. Concept drift detection can indeed assist in promptly identifying instances where model performance deteriorates or when there are changes in data distribution. This paper focuses on the problem of concept drift detection in order to conduct frequent pattern mining. To address the limitation of fixed sliding windows in adapting to evolving data streams, we propose a variable sliding window frequent pattern mining algorithm, which dynamically adjusts the window size to adapt to new concept drifts and detect them in a timely manner. Furthermore, considering the challenge of existing concept drift detection algorithms that struggle to adapt to different types of drifting data simultaneously, we introduce an additional dual-layer embedded variable sliding window. This approach helps differentiate types of concept drift and incorporates a decay model for drift adaptation. The proposed algorithm can effectively detect different types of concept drift in data streams, perform targeted drift adaptation, and exhibit efficiency in terms of time complexity and memory consumption. Additionally, the algorithm maintains stable performance, avoiding abrupt changes due to window size variations and ensuring overall robustness. [ABSTRACT FROM AUTHOR]

Ensuring data security in cloud computing is crucial due to the growing reliance on cloud-based services. Hybrid cryptography and image steganography have emerged as robust techniques to enhance data confidentiality in the cloud. In this research paper, we propose a novel algorithm, "Machine Learning-Enhanced Hybrid Cryptography and Image Steganography," integrating these methods to provide comprehensive data protection. The algorithm employs key generation, encryption, steganography, cloud storage, data retrieval, and machine learning-based attack detection to defend against advanced cyber threats. Our experimentation demonstrates the algorithm's effectiveness in detecting DoS attacks, data breaches, and data leakage attempts using SVM, Neural Network, Isolation Forest, and Random Forest models. The proposed approach offers broad applicability, fortifying data security and fostering further advancements in cloud security research. [ABSTRACT FROM AUTHOR]

Cloud computing has revolutionized data and resource management, enabling organizations to optimize their operations and reduce costs. However, this technology has also given rise to new security threats and challenges that require urgent attention. This article provides a comprehensive analysis of emerging threats in cloud computing, such as data leakage, account hijacking, and denial-of-service (DDoS) attacks, and highlights challenges in security management, such as operational complexity, lack of visibility over resources, and difficulties in complying with regulations. In addition, mitigation strategies are proposed, including rigorous security policies, continuous monitoring, and close collaboration with cloud service providers. Cloud security is a multifaceted challenge that demands a comprehensive approach to protect data, maintain service integrity, and ensure business continuity. [ABSTRACT FROM AUTHOR]

Deep learning techniques have shown significant impact in enhancing security across various domains by leveraging artificial neural networks models. When applied to cloud computing security, deep learning offers cost-effective solutions by automating threat detection, reducing manual monitoring, and improving overall security effectiveness. Deep learning models using neural networks play crucial role in tasks like intrusion detection, malware detection, anomaly detection, and log analysis. Integration of deep learning into cloud security requires careful evaluation of existing systems, defining objectives, dataset selection and preparation, model tuning, and eventual modifications for compatibility. Furthermore, implementing deep learning techniques in cloud security entails considering factors such as computational resources, data collection and preparation costs, model development, integration efforts, and ongoing monitoring and maintenance. This paper proposes a feed-forward propagation Artificial Neural Network (ANN) model in cloud security and investigates the key steps for integrating such models into cloud security strategies. Considering that the effectiveness of the ANN model depends on factors such as training data quality, network architecture, and weight adjustment algorithms, the study utilizes a dataset from Kaggle.com for validation and demonstrates steps involved in training and evaluation of the ANN model. [ABSTRACT FROM AUTHOR]